Overview

Dataset statistics

Number of variables9
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory70.4 KiB
Average record size in memory72.1 B

Variable types

Numeric7
Categorical2

Alerts

id is uniformly distributedUniform
id has unique valuesUnique

Reproduction

Analysis started2024-07-01 10:00:44.315430
Analysis finished2024-07-01 10:00:58.090652
Duration13.78 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

id
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean500.5
Minimum1
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-07-01T15:00:58.387433image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile50.95
Q1250.75
median500.5
Q3750.25
95-th percentile950.05
Maximum1000
Range999
Interquartile range (IQR)499.5

Descriptive statistics

Standard deviation288.81944
Coefficient of variation (CV)0.57706181
Kurtosis-1.2
Mean500.5
Median Absolute Deviation (MAD)250
Skewness0
Sum500500
Variance83416.667
MonotonicityStrictly increasing
2024-07-01T15:00:58.689646image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
672 1
 
0.1%
659 1
 
0.1%
660 1
 
0.1%
661 1
 
0.1%
662 1
 
0.1%
663 1
 
0.1%
664 1
 
0.1%
665 1
 
0.1%
666 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
1000 1
0.1%
999 1
0.1%
998 1
0.1%
997 1
0.1%
996 1
0.1%
995 1
0.1%
994 1
0.1%
993 1
0.1%
992 1
0.1%
991 1
0.1%

age
Real number (ℝ)

Distinct52
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.783
Minimum18
Maximum69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-07-01T15:00:59.069426image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile20
Q130
median45
Q357
95-th percentile67
Maximum69
Range51
Interquartile range (IQR)27

Descriptive statistics

Standard deviation15.042213
Coefficient of variation (CV)0.34356287
Kurtosis-1.1926685
Mean43.783
Median Absolute Deviation (MAD)13
Skewness-0.046000137
Sum43783
Variance226.26818
MonotonicityNot monotonic
2024-07-01T15:00:59.656132image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47 32
 
3.2%
30 30
 
3.0%
33 29
 
2.9%
45 29
 
2.9%
53 28
 
2.8%
19 28
 
2.8%
69 28
 
2.8%
64 27
 
2.7%
41 25
 
2.5%
61 24
 
2.4%
Other values (42) 720
72.0%
ValueCountFrequency (%)
18 21
2.1%
19 28
2.8%
20 12
1.2%
21 14
1.4%
22 22
2.2%
23 23
2.3%
24 11
 
1.1%
25 13
1.3%
26 21
2.1%
27 20
2.0%
ValueCountFrequency (%)
69 28
2.8%
68 19
1.9%
67 10
 
1.0%
66 15
1.5%
65 14
1.4%
64 27
2.7%
63 20
2.0%
62 20
2.0%
61 24
2.4%
60 23
2.3%

gender
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Male
357 
Other
327 
Female
316 

Length

Max length6
Median length5
Mean length4.959
Min length4

Characters and Unicode

Total characters4959
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowFemale
4th rowOther
5th rowFemale

Common Values

ValueCountFrequency (%)
Male 357
35.7%
Other 327
32.7%
Female 316
31.6%

Length

2024-07-01T15:00:59.944027image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-01T15:01:00.205295image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
male 357
35.7%
other 327
32.7%
female 316
31.6%

Most occurring characters

ValueCountFrequency (%)
e 1316
26.5%
a 673
13.6%
l 673
13.6%
M 357
 
7.2%
O 327
 
6.6%
t 327
 
6.6%
h 327
 
6.6%
r 327
 
6.6%
F 316
 
6.4%
m 316
 
6.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4959
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1316
26.5%
a 673
13.6%
l 673
13.6%
M 357
 
7.2%
O 327
 
6.6%
t 327
 
6.6%
h 327
 
6.6%
r 327
 
6.6%
F 316
 
6.4%
m 316
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4959
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1316
26.5%
a 673
13.6%
l 673
13.6%
M 357
 
7.2%
O 327
 
6.6%
t 327
 
6.6%
h 327
 
6.6%
r 327
 
6.6%
F 316
 
6.4%
m 316
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4959
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1316
26.5%
a 673
13.6%
l 673
13.6%
M 357
 
7.2%
O 327
 
6.6%
t 327
 
6.6%
h 327
 
6.6%
r 327
 
6.6%
F 316
 
6.4%
m 316
 
6.4%

income
Real number (ℝ)

Distinct996
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88500.8
Minimum30004
Maximum149973
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-07-01T15:01:00.462552image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum30004
5-th percentile36274.9
Q157911.75
median87845.5
Q3116110.25
95-th percentile143433.4
Maximum149973
Range119969
Interquartile range (IQR)58198.5

Descriptive statistics

Standard deviation34230.771
Coefficient of variation (CV)0.38678488
Kurtosis-1.1673027
Mean88500.8
Median Absolute Deviation (MAD)29250.5
Skewness0.051064873
Sum88500800
Variance1.1717457 × 109
MonotonicityNot monotonic
2024-07-01T15:01:00.767352image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49020 2
 
0.2%
39407 2
 
0.2%
137307 2
 
0.2%
61376 2
 
0.2%
54274 1
 
0.1%
59108 1
 
0.1%
87717 1
 
0.1%
48576 1
 
0.1%
135394 1
 
0.1%
99342 1
 
0.1%
Other values (986) 986
98.6%
ValueCountFrequency (%)
30004 1
0.1%
30058 1
0.1%
30074 1
0.1%
30102 1
0.1%
30121 1
0.1%
30374 1
0.1%
30492 1
0.1%
30523 1
0.1%
30570 1
0.1%
30708 1
0.1%
ValueCountFrequency (%)
149973 1
0.1%
149936 1
0.1%
149744 1
0.1%
149741 1
0.1%
149723 1
0.1%
149578 1
0.1%
149505 1
0.1%
149256 1
0.1%
149062 1
0.1%
148784 1
0.1%

spending_score
Real number (ℝ)

Distinct100
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.685
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-07-01T15:01:01.084163image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q126
median50
Q376
95-th percentile96
Maximum100
Range99
Interquartile range (IQR)50

Descriptive statistics

Standard deviation28.955175
Coefficient of variation (CV)0.57127701
Kurtosis-1.2160064
Mean50.685
Median Absolute Deviation (MAD)25
Skewness-0.016577324
Sum50685
Variance838.40218
MonotonicityNot monotonic
2024-07-01T15:01:01.399983image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70 20
 
2.0%
27 19
 
1.9%
96 17
 
1.7%
100 17
 
1.7%
83 16
 
1.6%
69 16
 
1.6%
19 15
 
1.5%
79 15
 
1.5%
68 15
 
1.5%
40 15
 
1.5%
Other values (90) 835
83.5%
ValueCountFrequency (%)
1 11
1.1%
2 14
1.4%
3 13
1.3%
4 11
1.1%
5 4
 
0.4%
6 13
1.3%
7 7
0.7%
8 7
0.7%
9 6
0.6%
10 9
0.9%
ValueCountFrequency (%)
100 17
1.7%
99 10
1.0%
98 6
 
0.6%
97 4
 
0.4%
96 17
1.7%
95 9
0.9%
94 5
 
0.5%
93 9
0.9%
92 11
1.1%
91 10
1.0%

membership_years
Real number (ℝ)

Distinct10
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.469
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-07-01T15:01:01.642159image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q38
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8557301
Coefficient of variation (CV)0.52216677
Kurtosis-1.2065871
Mean5.469
Median Absolute Deviation (MAD)2
Skewness0.02984401
Sum5469
Variance8.1551942
MonotonicityNot monotonic
2024-07-01T15:01:01.864349image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
5 125
12.5%
2 109
10.9%
9 108
10.8%
6 96
9.6%
10 96
9.6%
3 95
9.5%
1 95
9.5%
7 94
9.4%
4 94
9.4%
8 88
8.8%
ValueCountFrequency (%)
1 95
9.5%
2 109
10.9%
3 95
9.5%
4 94
9.4%
5 125
12.5%
6 96
9.6%
7 94
9.4%
8 88
8.8%
9 108
10.8%
10 96
9.6%
ValueCountFrequency (%)
10 96
9.6%
9 108
10.8%
8 88
8.8%
7 94
9.4%
6 96
9.6%
5 125
12.5%
4 94
9.4%
3 95
9.5%
2 109
10.9%
1 95
9.5%

purchase_frequency
Real number (ℝ)

Distinct50
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.596
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-07-01T15:01:02.118413image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q115
median27
Q339
95-th percentile49
Maximum50
Range49
Interquartile range (IQR)24

Descriptive statistics

Standard deviation14.243654
Coefficient of variation (CV)0.53555623
Kurtosis-1.1301031
Mean26.596
Median Absolute Deviation (MAD)12
Skewness-0.083965956
Sum26596
Variance202.88167
MonotonicityNot monotonic
2024-07-01T15:01:02.414527image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28 35
 
3.5%
42 30
 
3.0%
27 30
 
3.0%
49 29
 
2.9%
31 27
 
2.7%
22 27
 
2.7%
24 26
 
2.6%
9 25
 
2.5%
19 23
 
2.3%
50 23
 
2.3%
Other values (40) 725
72.5%
ValueCountFrequency (%)
1 13
1.3%
2 21
2.1%
3 19
1.9%
4 11
1.1%
5 23
2.3%
6 22
2.2%
7 11
1.1%
8 20
2.0%
9 25
2.5%
10 15
1.5%
ValueCountFrequency (%)
50 23
2.3%
49 29
2.9%
48 19
1.9%
47 22
2.2%
46 22
2.2%
45 17
1.7%
44 21
2.1%
43 14
1.4%
42 30
3.0%
41 22
2.2%
Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Electronics
215 
Sports
210 
Home & Garden
206 
Groceries
199 
Clothing
170 

Length

Max length13
Median length9
Mean length9.454
Min length6

Characters and Unicode

Total characters9454
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGroceries
2nd rowSports
3rd rowClothing
4th rowHome & Garden
5th rowElectronics

Common Values

ValueCountFrequency (%)
Electronics 215
21.5%
Sports 210
21.0%
Home & Garden 206
20.6%
Groceries 199
19.9%
Clothing 170
17.0%

Length

2024-07-01T15:01:02.753249image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-01T15:01:03.094965image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
electronics 215
15.2%
sports 210
14.9%
home 206
14.6%
206
14.6%
garden 206
14.6%
groceries 199
14.1%
clothing 170
12.0%

Most occurring characters

ValueCountFrequency (%)
r 1029
10.9%
e 1025
10.8%
o 1000
 
10.6%
c 629
 
6.7%
s 624
 
6.6%
t 595
 
6.3%
n 591
 
6.3%
i 584
 
6.2%
412
 
4.4%
G 405
 
4.3%
Other values (12) 2560
27.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9454
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 1029
10.9%
e 1025
10.8%
o 1000
 
10.6%
c 629
 
6.7%
s 624
 
6.6%
t 595
 
6.3%
n 591
 
6.3%
i 584
 
6.2%
412
 
4.4%
G 405
 
4.3%
Other values (12) 2560
27.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9454
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 1029
10.9%
e 1025
10.8%
o 1000
 
10.6%
c 629
 
6.7%
s 624
 
6.6%
t 595
 
6.3%
n 591
 
6.3%
i 584
 
6.2%
412
 
4.4%
G 405
 
4.3%
Other values (12) 2560
27.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9454
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 1029
10.9%
e 1025
10.8%
o 1000
 
10.6%
c 629
 
6.7%
s 624
 
6.6%
t 595
 
6.3%
n 591
 
6.3%
i 584
 
6.2%
412
 
4.4%
G 405
 
4.3%
Other values (12) 2560
27.1%

last_purchase_amount
Real number (ℝ)

Distinct994
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean492.34867
Minimum10.4
Maximum999.74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-07-01T15:01:03.381545image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum10.4
5-th percentile45.0495
Q1218.7625
median491.595
Q3747.17
95-th percentile944.253
Maximum999.74
Range989.34
Interquartile range (IQR)528.4075

Descriptive statistics

Standard deviation295.74425
Coefficient of variation (CV)0.60068051
Kurtosis-1.2739791
Mean492.34867
Median Absolute Deviation (MAD)266.62
Skewness0.017553616
Sum492348.67
Variance87464.663
MonotonicityNot monotonic
2024-07-01T15:01:03.830827image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
559.73 2
 
0.2%
553.35 2
 
0.2%
66.6 2
 
0.2%
140.52 2
 
0.2%
937.86 2
 
0.2%
532.51 2
 
0.2%
758.44 1
 
0.1%
566.59 1
 
0.1%
760.27 1
 
0.1%
900.16 1
 
0.1%
Other values (984) 984
98.4%
ValueCountFrequency (%)
10.4 1
0.1%
11.01 1
0.1%
11.29 1
0.1%
12.36 1
0.1%
12.45 1
0.1%
13.16 1
0.1%
13.46 1
0.1%
13.69 1
0.1%
14.52 1
0.1%
15.04 1
0.1%
ValueCountFrequency (%)
999.74 1
0.1%
998.98 1
0.1%
998.51 1
0.1%
998.09 1
0.1%
997.24 1
0.1%
997.15 1
0.1%
994.1 1
0.1%
992.17 1
0.1%
991.93 1
0.1%
990.87 1
0.1%

Interactions

2024-07-01T15:00:55.665283image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T15:00:45.096956image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T15:00:46.800318image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T15:00:48.655161image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T15:00:50.304017image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T15:00:52.023444image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T15:00:54.167928image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T15:00:55.872458image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T15:00:45.512476image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T15:00:47.224232image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T15:00:48.891363image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T15:00:50.511504image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T15:00:52.704650image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T15:00:54.399026image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T15:00:56.091990image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T15:00:45.723542image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T15:00:47.491029image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T15:00:49.133298image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T15:00:50.726463image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T15:00:52.931699image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T15:00:54.587494image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T15:00:56.315062image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T15:00:45.938704image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T15:00:47.716078image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T15:00:49.358682image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T15:00:50.939474image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T15:00:53.159466image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T15:00:54.815456image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T15:00:56.535803image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T15:00:46.159183image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T15:00:47.961226image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T15:00:49.602839image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T15:00:51.151239image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T15:00:53.374377image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T15:00:55.026286image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T15:00:56.748473image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T15:00:46.370955image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T15:00:48.200270image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T15:00:49.805604image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T15:00:51.382768image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T15:00:53.587837image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T15:00:55.219992image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T15:00:56.952036image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T15:00:46.583848image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T15:00:48.439514image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T15:00:50.049974image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T15:00:51.756160image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T15:00:53.958546image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T15:00:55.454069image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-07-01T15:00:57.250681image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-01T15:00:57.602895image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idagegenderincomespending_scoremembership_yearspurchase_frequencypreferred_categorylast_purchase_amount
0138Female9934290324Groceries113.53
1221Female7885260242Sports41.93
2360Female12657330228Clothing424.36
3440Other470997495Home & Garden991.93
4565Female14062121325Electronics347.08
5631Other5730524330Home & Garden86.85
6719Other5431968543Clothing191.72
7843Male10811594927Groceries734.56
8953Male344242967Sports951.71
91055Female458395572Electronics821.18
idagegenderincomespending_scoremembership_yearspurchase_frequencypreferred_categorylast_purchase_amount
99099121Other8026441426Sports458.93
99199253Other8357897550Sports917.08
99299322Female12547957827Home & Garden139.75
99399429Female6479942519Electronics967.26
99499560Male7192325240Clothing986.97
99599657Male1121705761Clothing313.64
99699723Other65337761023Groceries632.83
99799823Male11309740542Sports75.09
99899922Female11369563744Electronics505.16
999100036Female904207231Groceries669.26